Methods for vehicle speed detection using video have many important transportation applications. For applications such as traffic speed enforcement, accurate speed detection is necessary. One method for determining a vehicle's speed is to capture two time-sequenced images of that vehicle, track a specific feature on that vehicle such as, for example, a location of the vehicle's license plate, and then calculate the vehicle's speed from trigonometric relationships. For accurate speed determination, the precise height above the road surface of the feature being tracked needs to be known in advanced, unless a stereo imaging system is used, wherein pairs of images from two different positions are captured. Unfortunately, vehicle features are not placed at fixed heights across all vehicle makes and models. Moreover, since video images are 2D representations of the 3D world, many points in real space map to a single point on the video image. Consequently, unless the camera is mounted at the same height above the road surface as the vehicle feature (or alternatively, at a much higher location above the ground, e.g. aerial view), the speed calculated is highly dependent on the height of the feature above the road.
A typical video speed detection system can be mounted on a pole or gantry anywhere from about 12 feet and about 25 feet above the road surface. Therefore, the height of the feature needs to be accurately known in order to perform height-compensation to the calculated speed. For example, a tracked feature which is 2 ft above the ground, the speed error due to ignoring the height is equal to 17% ( 2/12) or 8% ( 2/25) if camera is mounted 12 ft or 25 ft above the ground, respectively. In order to achieve 1% accuracy, the tracked feature height would need to be known to within about an inch or two in typical use conditions. Unfortunately, there is no standard vehicle feature that is at a fixed height for vehicles of all types, makes and models. As such, speeds calculated by analyzing non-stereo images taken of moving vehicles tend to lack the accuracy required for law enforcement.
One way of avoiding this problem is to select a feature which is at “zero height,” which is typically the interface between the tires and the road. The challenge to this is the detection reliability, as the contrast between the tires and the pavement is typically very low, because both materials exhibit high absorbances at visible or near-IR wavelengths. This problem is further complicated by shadows in the region of interest. Even with image enhancement (as shown in FIG. 2b), the signal is still very weak.
Attempts have been proposed to overcome this difficulty by using infrared cameras operating at selected wavelengths, where tires and road asphalt show different absorbance features (e.g., a two-band camera system operating at 5.5 μm and 6.4 μm). This may enhance the image contrast; however, the camera system required is non-conventional and would likely be costly.
Accordingly, what is needed are improved systems and methods for analyzing images of moving vehicles to determine the vehicle's speed that overcome the difficulties of the conventional approaches.